Babbar-Sebens, MeghnaBruder, Slawa RomanaJacinthe, Pierre-AndreTedesco, Lenore P.2012-10-292012-10-292012-10-29https://hdl.handle.net/1805/3043http://dx.doi.org/10.7912/C2/529Indiana University-Purdue University Indianapolis (IUPUI)In this research, Environmental Fluid Dynamic Code (EFDC) and Adaptive- Networkbased Fuzzy Inference System Models (ANFIS) were developed and implemented to determine the spatial-temporal distribution of cyanobacterial metabolites: 2-MIB and geosmin, in Eagle Creek Reservoir, IN. The research is based on the current need for understanding algae dynamics and developing prediction methods for algal taste and odor release events. In this research the methodology for prediction of 2-MIB and geosmin production was explored. The approach incorporated a combination of numerical and heuristic modeling to show its capabilities in prediction of cyanobacteria metabolites. The reservoir’s variable data measured at monitoring stations and consisting of chemical/physical and biological parameters with the addition of calculated mixing conditions within the reservoir were used to train and validate the models. The Adaptive – Network based Fuzzy Inference System performed satisfactorily in predicting the metabolites, in spite of multiple model constraints. The predictions followed the generally observed trends of algal metabolites during the three seasons over three years (2008-2010). The randomly selected data pairs for geosmin for validation achieved coefficient of determination of 0.78, while 2-MIB validation was not accepted due to large differences between two observations and their model prediction. Although, these ANFIS results were accepted, the further application of the ANFIS model coupled with the numerical models to predict spatio-temporal distribution of metabolites showed serious limitations, due to numerical model calibration errors. The EFDC-ANFIS model over-predicted Pseudanabaena spp. biovolumes for selected stations. The predicted value was 18,386,540 mm3/m3, while observed values were 942,478 mm3/m3. The model simulating Planktothrix agardhii gave negative biovolumes, which were assumed to represent zero values observed at the station. The taste and odor metabolite, geosmin, was under-predicted as the predicted v concentration was 3.43 ng/L in comparison to observed value of 11.35 ng/l. The 2-MIB model did not validate during EFDC to ANFIS model evaluation. The proposed approach and developed methodology could be used for future applications if the limitations are appropriately addressed.enAlgal bloom, cyanobacteria, EFDC, modeling, fuzzy logicAlgal blooms -- MonitoringFuzzy logicMicrobial toxins -- BiotechnologyCyanobacterial toxinsWater -- Purification -- Taste and odor controlMicrobial ecologyMetabolites -- IdentificationAlgae -- ControlAlgae -- GrowthReservoirs -- Indiana -- IndianapolisHydrodynamics -- Mathematical modelsHeuristic programmingEnvironmental risk assessmentReservoirs -- Indiana -- ClassificationThesis